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International Conference on Wireless Algorithms, Systems, and Applications

WASA 2018: Wireless Algorithms, Systems, and Applications pp 509–520Cite as

Privacy-Preserving Personal Sensitive Data in Crowdsourcing

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10874))

Abstract

Spatial crowdsourcing system refers to sending various location-based tasks to workers according to their positions, and workers need to physically move to specified locations to accomplish tasks. The workers are restricted to report their real-time sensitive position to the server so as to keep in coordination with the crowdsourcing server. Therefore, implementing crowdsourcing system while preserving the privacy of workers sensitive information is a key issue that needs to be tackled. We discard the assumption of a trustworthy third party cellular service provider (CSP), and further propose a local method to achieve acceptable results. A differential privacy model ensures rigorous privacy guarantee, and Laplace mechanism noise is introduced to preserve workers sensitive information. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real-world datasets.

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Acknowledgments

This work is partially supported by National Natural Science Foundation of China (NSFC) under Grant No. 61772491, No. 61472460, and Natural Science Foundation of Jiangsu Province under Grant No. BK20161256. Kai Han is the corresponding author.

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Authors and Affiliations

  1. School of Computer Science and Technology/Suzhou Institute for Advanced Study, University of Science and Technology of China, Hefei, People’s Republic of China

    Ke Xu, Kai Han, Hang Ye, Feng Gao & Chaoting Xu

Authors
  1. Ke Xu
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  2. Kai Han
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  3. Hang Ye
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  4. Feng Gao
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  5. Chaoting Xu
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Corresponding author

Correspondence to Kai Han .

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Editors and Affiliations

  1. University of South Florida, Tampa, Florida, USA

    Sriram Chellappan

  2. Virginia Commonwealth University, Richmond, Virginia, USA

    Wei Cheng

  3. Georgia State University, Atlanta, Georgia, USA

    Wei Li

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Xu, K., Han, K., Ye, H., Gao, F., Xu, C. (2018). Privacy-Preserving Personal Sensitive Data in Crowdsourcing. In: Chellappan, S., Cheng, W., Li, W. (eds) Wireless Algorithms, Systems, and Applications. WASA 2018. Lecture Notes in Computer Science(), vol 10874. Springer, Cham. https://doi.org/10.1007/978-3-319-94268-1_42

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  • DOI: https://doi.org/10.1007/978-3-319-94268-1_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94267-4

  • Online ISBN: 978-3-319-94268-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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